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Updated: Jun 24, 2025

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Exome sequence analysis identifies rare coding variants associated with a machine learning-based marker for coronary

Ben Omega Petrazzini1,2,3, Iain S Forrest1,2,4, Ghislain Rocheleau1,2,3

  • 1The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Nature Genetics
|June 11, 2024
PubMed

Insights

This study used an in silico score for coronary artery disease (CAD) to identify genetic variants associated with the disease. Findings reveal new gene associations, enhancing our understanding of CAD

Area of Science:

  • Genetics and Bioinformatics
  • Cardiovascular Disease Research
  • Computational Biology

Background:

  • Coronary artery disease (CAD) is a complex condition influenced by various risk factors and pathological processes.
  • An in silico score, derived from machine learning and electronic health records, can quantify CAD progression, severity, and underdiagnosis.
  • This digital marker holds potential for improving genetic discovery in CAD.

Purpose of the Study:

  • To investigate the association between rare and ultrarare coding variants and an in silico CAD score.
  • To identify novel genetic contributors to coronary artery disease.
  • To explore the utility of digital health markers in genetic association studies.

Main Methods:

  • Utilized UK Biobank, All of Us Research Program, and BioMe Biobank data.
  • Performed association tests between rare/ultrarare coding variants and the in silico CAD score.
  • Evaluated identified genes for existing genetic, biological, or clinical support for CAD.

Main Results:

  • Identified significant associations in 17 genes with the in silico CAD score.
  • Validated 14 of these genes with prior evidence supporting their role in CAD.
  • Observed an enrichment of ultrarare coding variants in 321 aggregated CAD-associated genes.

Conclusions:

  • The study expands the understanding of the genetic basis of coronary artery disease.
  • Digital markers derived from electronic health records can effectively enhance genetic association studies for complex diseases like CAD.
  • Further discoveries of ultrarare variant associations in CAD are anticipated.

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